Citations

ABSTRACT Aims Opioid use disorder remains a major public health crisis in the United States, driven largely by rising overdose deaths from synthetic opioids, such as fentanyl. Emerging population‐level evidence suggests that GLP‐1 receptor agonists (GLP‐1RAs) may reduce overdose risk. However, their underlying molecular mechanisms are not well understood. We investigated the transcriptional effects of fentanyl and GLP‐1RAs using human induced pluripotent stem cell (iPSC)‐derived forebrain organoids and neurons. Methods Organoids were treated with fentanyl, liraglutide, or exenatide, followed by RNA sequencing. We extended these analyses to iPSC‐derived neurons exposed to additional therapeutic candidates, including the anticonvulsants topiramate and gabapentin, and the metabolic modulator β‐hydroxybutyrate. All compounds were tested at clinically relevant concentrations, and transcriptomic and functional genomic assays were performed. Results Across models and drug classes, we identified modulation of endoplasmic reticulum (ER) stress signaling as a shared molecular mechanism. Fentanyl and GLP‐1RAs consistently downregulated ER stress‐related genes, with TRIB3 as the most strongly suppressed target. We further identified CEBPB as a key upstream regulator and confirmed reduced CEBPB DNA‐binding activity in ER stress‐related genes. Conclusion These findings highlight ER stress modulation as a convergent pathway with potential therapeutic relevance for substance use disorders.

Background: Several breast cancer (BC) risk prediction models have been developed to provide personal risk assessments. Though individually validated, their performance has not been systematically evaluated across a wide range of populations or ages. Methods: We harmonized individual-level baseline questionnaire data and incident BC diagnoses from 21 cohorts from North America, Europe, and Australia participating in the Breast Cancer Risk Prediction Project. Five-year absolute risk of invasive BC was estimated for five established risk prediction models using classical risk factors only. Discrimination was evaluated by area under the curve (AUC). Calibration was assessed using average and risk-decile specific expected to observed (E/O) ratios. Performance metrics were meta-analyzed across cohorts and models. Metaregression tested associations between cohort characteristics and performance metrics. Results: This analysis included 1,595,977 women aged 20-75 years, enrolled in studies between 1976-2015, with 19,062 (1.2%) invasive BC cases ascertained within 5 years from exposure assessment. Age-adjusted AUCs were similar across models and cohorts (pooled AUCs by model: 0.57-0.58), while E/O ratios varied substantially (pooled E/O ratios by model: 0.83-1.25). Overestimation was common among predicted high-risk individuals (>3%). No appreciable differences in model performance by cohort age, birth year, race, and variable missingness emerged. Calibration improved after assigning race-specific incidence rates. Conclusion: Existing BC risk prediction models provided similar risk discrimination across multiple cohorts, although there was overestimation of risk for high-risk individuals. Performance variation across cohorts was not driven by specific characteristics, which supports development of a unified risk model for diverse populations that leverages appropriate incidence rates.

Atelectasis, the partial or complete collapse of the lung, requires early diagnosis for effective treatment. This study investigates the effects of Region of Interest (ROI) cropping and transfer learning on the automatic classification of atelectasis from chest X-ray images. Four SqueezeNet-based models were developed using two image types (raw and cropped lung regions) and two training strategies (from-scratch and transfer learning). Models I and II were trained from scratch on raw and cropped images, respectively, while Models III and IV employed transfer learning on the same image sets. Performance was evaluated using standard classification metrics. Model IV, combining transfer learning and ROI-cropped images, achieved the best performance, with 98.43% accuracy, 0.984 F1-score, and 0.999 AUC. It also reached 0.9861 precision, 0.9828 sensitivity, and 0.9859 specificity. ROI-based cropping consistently improved classification performance. Combining transfer learning with ROI-focused preprocessing significantly enhances atelectasis detection and shows promise as a reliable clinical decision-support approach.

PURPOSE EGFR alterations occur in a subset of biliary tract cancers (BTCs) and are often linked to disease progression and poor prognosis, yet targeted approaches remain underexplored. ERRFI1 , encoding the epidermal growth factor receptor (EGFR) inhibitor MIG6, is mutated in various cancers, including BTC, representing a potential predictive biomarker. We investigated whether loss of ERRFI1 function may enhance sensitivity to EGFR-targeted tyrosine kinase inhibitors (TKIs), presenting a potential therapeutic opportunity. METHODS This is a retrospective multicenter study of patients with BTC harboring ERRFI1 alterations. Data were extracted from electronic health records following institutional approval. ERRFI1 alterations were classified based on the alteration type, variant allele frequencies were documented, and coalterations were analyzed. Treatment outcomes including response, time to progression (TTP), overall survival (OS), safety profiles, and tumor marker kinetics were noted. RESULTS Fourteen BTC patients with ERRFI1 mutations were identified in our database; the median age was 50 years, 64% were males; all cases were intrahepatic cholangiocarcinomas. Common coalterations included IDH1 (36%), TP53 (29%), and ARID1A (29%). All patients received gemcitabine-cisplatin as first-line treatment, and 79% received immune checkpoint inhibitors. Eight patients (57%) received EGFR TKIs. Best responses to EGFR therapy included three partial responses (2 responses lasting >20 months), four stable diseases, and one progressive disease. The median TTP was 7 months (95% CI, 6 to 21 months). Treatment was well-tolerated: toxicities included rash (n = 2), transaminitis (n = 1), and diarrhea (n = 1). The median OS was 20 months (95% CI: 8-36 months); 50% remained alive at last follow-up. CONCLUSION ERRFI1 mutations represent actionable biomarkers in BTC, with EGFR-targeted therapy demonstrating meaningful clinical benefit in this heavily pretreated population. These findings support integration of ERRFI1 testing into routine molecular profiling of BTC and other EGFR-driven malignancies.

GGGGCC (G 4 C 2 ) repeat expansion in C9ORF72 is the most common genetic cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Toxicity is thought to result from the accumulation of either repeat RNAs and/or dipeptide repeat proteins (DPRs) translated from repeat-containing transcripts through repeat-associated non-AUG (RAN) translation. To disentangle RNA from DPR toxicity, we mutated a CUG codon predominantly used to initiate DPR translation from all three reading frames. This mutation disrupted DPR synthesis while preserving the expression of repeat-containing RNAs. Despite the accumulation of RNA foci, behavioral deficits and pathological abnormalities, including p-TDP-43 inclusions, STING activation, motor neuron loss, neuroinflammation, and increased plasma neurofilament concentration, were alleviated in C9ORF72 mice. Base editing of the CUG codon also improved molecular phenotypes and survival in patient induced pluripotent stem cell–derived neurons, which highlights the potential of therapeutically targeting DPR production rather than repeat RNAs.

Abstract Background Eligibility criteria for clinical trials are crucial for maintaining safety and study integrity. However, overly restrictive criteria can result in unrepresentative trial populations, leading to gaps in understanding real‐world treatment efficacy. Addressing potentially modifiable exclusions (PMEs) could enhance trial accessibility and participation without compromising safety. Methods The authors retrospectively analyzed screen‐failed patients with biliary tract cancer or pancreatic cancer between August 2019 and November 2024 at The University of Texas MD Anderson Cancer Center. Screen‐failed patients were those who provided informed consent but did not participate for any reason. Clinical data were obtained from screening logs and electronic health records. PMEs were identified and validated by two independent medical oncologists. Results Of 585 screen‐failed patients from 18 trials, 509 were analyzed (367 with pancreatic cancer and 142 with biliary tract cancer) after excluding 76 because of incomplete data. Leading causes of screen failure were declined participation (19%), comorbidities (12%), suboptimal organ function (11%), absence of a biomarker (10%), and insufficient biospecimens (7%). Reasons for declining included preference for standard care (23%), travel (22%), and competing trials (5%). The authors identified 69 patients (13.6%) who had PMEs (primarily borderline laboratory abnormalities), including liver function (23%), kidney function (20%), platelet count (12%), hemoglobin (7%), and white blood cell count (6%) abnormalities. Other PMEs included previous or concurrent malignancies (9%) and viral hepatitis (4%). PMEs were evenly distributed across trials. Conclusions Rigid eligibility criteria exclude stable patients who might benefit from investigational treatments. Easing criteria related to incidental or asymptomatic laboratory abnormalities could broaden trial accessibility and improve enrollment in populations that are more representative of real‐world use.

Background: Substance use disorders (SUDs) are highly prevalent, affecting over 48.5 million Americans. Available treatments for SUD remain insufficient, and many patients do not respond to existing interventions despite adequate adherence to treatments. While novel therapies for SUD are urgently needed, the use of psychedelic drugs for the treatment of SUDs has shown promise. Objectives: This overview of systematic reviews summarizes existing evidence on hallucinogens—serotonergic psychedelics and ketamine—for the treatment of SUD. Methods: A comprehensive search of the literature was conducted to identify relevant evidence for using serotonergic and non-serotonergic psychedelics for the treatment of SUDs. After initial screening (n = 468 studies), 62 studies were retrieved and assessed for eligibility, and a total of 16 systematic reviews were included. Conclusions: Although preliminary, evidence suggests that the use of serotonergic and non-serotonergic psychedelics for the treatment of SUD may provide advantages over traditional therapeutics, and these compounds may eventually become part of the next generation of treatments for SUD under specific circumstances. Research with these drugs has faced significant challenges, though, and caution when interpreting results is warranted, given high risk of bias and several other methodological limitations from the studies to date. Furthermore, risks associated with these drugs are not negligible. For now, the use of psychedelic drugs for the treatment of SUDs remains experimental, and existing evidence is insufficient to support its use in clinical practice.

Background/Objectives: Glucagon-like peptide-1 receptor agonists (GLP-1RAs), which were originally developed for managing type 2 diabetes by enhancing insulin secretion and reducing appetite, have emerged as promising candidates in alcohol use disorder (AUD). These medications offer a dual mechanism of action that aligns with the multifaceted nature of addiction by targeting both peripheral metabolic and central reward pathways. This review focused on the current clinical trials and real-world evidence regarding the effects of GLP-1RAs as novel therapeutics for AUD. We also discussed early but encouraging results from clinical trials in AUD, observational and real-world evidence, safety profiles, psychiatric considerations, and future directions leading beyond GLP-1RAs. Methods: A comprehensive English-language literature search was conducted per PRISMA guidelines across PubMed, Medline, Google Scholar, Web of Science, and trial registries. Using targeted keywords, we identified relevant clinical and observational studies on GLP-1RAs for alcohol use disorder, excluding off-topic or non-English works and assessing all studies for eligibility. Results: Out of 1080 records identified, seven studies met the inclusion criteria. The findings from recent clinical trials, large-scale observational studies, and real-world evidence suggest that GLP-1RAs may significantly reduce alcohol consumption, cravings, and alcohol-related hospitalizations. Their central effect on reward processing, coupled with a generally favorable safety profile, supports their potential therapeutic role beyond metabolic disorders. Conclusions: Emerging evidence positions GLP-1RAs as a promising new pharmacologic approach for managing AUD. Ongoing and future research should prioritize larger, longer-duration randomized controlled trials that include diverse populations, with specific attention to treatment motivation, co-occurring psychiatric conditions, and long-term outcomes.